基于大数据分析和聚类算法的电力用户行为特征挖掘  

Mining of Behavioral Characteristics of Power Users Based on Big Data Analysis and Clustering Algorithms

作　　者：彭振国 何乃彩 邵慧 张兆师 PENG Zhenguo;HE Naicai;SHAO Hui;ZHANG Zhaoshi(Gansu Tongxing Intelligent Technology Development Co.,Ltd.,Lanzhou Gansu 730050,China)

机构地区：[1]甘肃同兴智能科技发展有限责任公司,甘肃兰州730050

出　　处：《信息与电脑》2025年第1期17-19,共3页Information & Computer

摘　　要：为实现对用户行为特征数据的深度挖掘,提高行为特征提取的数量,研究引进了大数据分析技术与聚类算法,对电力用户行为特征的挖掘方法展开设计研究。从电力系统数据库中收集了大量的用户用电数据,构建了用户行为画像。引进K-means算法处理这些行为数据,以计算用户之间的相似度并实现聚类分析。同时,引进主成分分析技术对数据进行降维处理,从而有效提取和分类特征数据。实验结果表明,所设计的方法不仅提高了电力用户行为特征挖掘的数量,还保证了挖掘的特征与用户行为之间的相似度维持在较高水平。In order to achieve deep mining of user behavior feature data and increase the quantity of behavior feature extraction,the study introduces big data analysis technology and clustering algorithms to design and study the mining methods of power user behavior features.A large amount of user electricity consumption data was collected from the power system database,and a user behavior profile was constructed.Introducing the K-means algorithm to process these behavioral data,in order to calculate the similarity between users and achieve clustering analysis.At the same time,principal component analysis technology is introduced to perform dimensionality reduction on the data,effectively extracting and classifying feature data.The experimental results show that the designed method not only increases the number of power user behavior feature mining,but also ensures that the similarity between the mined features and user behavior is maintained at a high level.

关 键 词：大数据分析 电力用户 行为特征 聚类算法 行为画像 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]